Armani  Conroy

Armani Conroy


Flutter's Complex Navigator 2.0 | Velocity X And V Router | In Hindi By Desi Programmer

In this video We are going to talk about Flutter’s nav 2.0 , routeInformationParser , router delegate and other packages that save us from Writting a lot of boiler plater code .

========== For this Video ==========

⏲ Time Stamps

  • 00:00 : Start
  • 00:20 : Differences
  • 01:26 : Bare Bone Code
  • 02:58: Awesome Packages
  • 04:38 : Remove #
  • 05:10 : Using Velocity_X
  • 19:45: V Router


What is GEEK

Buddha Community

Flutter's Complex Navigator 2.0 | Velocity X And V Router | In Hindi By Desi Programmer

Google's Flutter 1.20 stable announced with new features - Navoki

Flutter Google cross-platform UI framework has released a new version 1.20 stable.

Flutter is Google’s UI framework to make apps for Android, iOS, Web, Windows, Mac, Linux, and Fuchsia OS. Since the last 2 years, the flutter Framework has already achieved popularity among mobile developers to develop Android and iOS apps. In the last few releases, Flutter also added the support of making web applications and desktop applications.

Last month they introduced the support of the Linux desktop app that can be distributed through Canonical Snap Store(Snapcraft), this enables the developers to publish there Linux desktop app for their users and publish on Snap Store.  If you want to learn how to Publish Flutter Desktop app in Snap Store that here is the tutorial.

Flutter 1.20 Framework is built on Google’s made Dart programming language that is a cross-platform language providing native performance, new UI widgets, and other more features for the developer usage.

Here are the few key points of this release:

Performance improvements for Flutter and Dart

In this release, they have got multiple performance improvements in the Dart language itself. A new improvement is to reduce the app size in the release versions of the app. Another performance improvement is to reduce junk in the display of app animation by using the warm-up phase.


If your app is junk information during the first run then the Skia Shading Language shader provides for pre-compilation as part of your app’s build. This can speed it up by more than 2x.

Added a better support of mouse cursors for web and desktop flutter app,. Now many widgets will show cursor on top of them or you can specify the type of supported cursor you want.

Autofill for mobile text fields

Autofill was already supported in native applications now its been added to the Flutter SDK. Now prefilled information stored by your OS can be used for autofill in the application. This feature will be available soon on the flutter web.


A new widget for interaction

InteractiveViewer is a new widget design for common interactions in your app like pan, zoom drag and drop for resizing the widget. Informations on this you can check more on this API documentation where you can try this widget on the DartPad. In this release, drag-drop has more features added like you can know precisely where the drop happened and get the position.

Updated Material Slider, RangeSlider, TimePicker, and DatePicker

In this new release, there are many pre-existing widgets that were updated to match the latest material guidelines, these updates include better interaction with Slider and RangeSliderDatePicker with support for date range and time picker with the new style.


New pubspec.yaml format

Other than these widget updates there is some update within the project also like in pubspec.yaml file format. If you are a flutter plugin publisher then your old pubspec.yaml  is no longer supported to publish a plugin as the older format does not specify for which platform plugin you are making. All existing plugin will continue to work with flutter apps but you should make a plugin update as soon as possible.

Preview of embedded Dart DevTools in Visual Studio Code

Visual Studio code flutter extension got an update in this release. You get a preview of new features where you can analyze that Dev tools in your coding workspace. Enable this feature in your vs code by _dart.previewEmbeddedDevTools_setting. Dart DevTools menu you can choose your favorite page embed on your code workspace.

Network tracking

The updated the Dev tools comes with the network page that enables network profiling. You can track the timings and other information like status and content type of your** network calls** within your app. You can also monitor gRPC traffic.

Generate type-safe platform channels for platform interop

Pigeon is a command-line tool that will generate types of safe platform channels without adding additional dependencies. With this instead of manually matching method strings on platform channel and serializing arguments, you can invoke native class and pass nonprimitive data objects by directly calling the Dartmethod.

There is still a long list of updates in the new version of Flutter 1.2 that we cannot cover in this blog. You can get more details you can visit the official site to know more. Also, you can subscribe to the Navoki newsletter to get updates on these features and upcoming new updates and lessons. In upcoming new versions, we might see more new features and improvements.

You can get more free Flutter tutorials you can follow these courses:

#dart #developers #flutter #app developed #dart devtools in visual studio code #firebase local emulator suite in flutter #flutter autofill #flutter date picker #flutter desktop linux app build and publish on snapcraft store #flutter pigeon #flutter range slider #flutter slider #flutter time picker #flutter tutorial #flutter widget #google flutter #linux #navoki #pubspec format #setup flutter desktop on windows

최 호민

최 호민


파이썬 코딩 무료 강의 - 이미지 처리, 얼굴 인식을 통한 캐릭터 씌우기를 해보아요

파이썬 코딩 무료 강의 (활용편6) - 이미지 처리, 얼굴 인식을 통한 캐릭터 씌우기를 해보아요

파이썬 무료 강의 (활용편6 - 이미지 처리)입니다.
OpenCV 를 이용한 다양한 이미지 처리 기법과 재미있는 프로젝트를 진행합니다.
누구나 볼 수 있도록 쉽고 재미있게 제작하였습니다. ^^

(0:00:00) 0.Intro
(0:00:31) 1.소개
(0:02:18) 2.활용편 6 이미지 처리 소개

[OpenCV 전반전]
(0:04:36) 3.환경설정
(0:08:41) 4.이미지 출력
(0:21:51) 5.동영상 출력 #1 파일
(0:29:58) 6.동영상 출력 #2 카메라
(0:34:23) 7.도형 그리기 #1 빈 스케치북
(0:39:49) 8.도형 그리기 #2 영역 색칠
(0:42:26) 9.도형 그리기 #3 직선
(0:51:23) 10.도형 그리기 #4 원
(0:55:09) 11.도형 그리기 #5 사각형
(0:58:32) 12.도형 그리기 #6 다각형
(1:09:23) 13.텍스트 #1 기본
(1:17:45) 14.텍스트 #2 한글 우회
(1:24:14) 15.파일 저장 #1 이미지
(1:29:27) 16.파일 저장 #2 동영상
(1:39:29) 17.크기 조정
(1:50:16) 18.이미지 자르기
(1:57:03) 19.이미지 대칭
(2:01:46) 20.이미지 회전
(2:06:07) 21.이미지 변형 - 흑백
(2:11:25) 22.이미지 변형 - 흐림
(2:18:03) 23.이미지 변형 - 원근 #1
(2:27:45) 24.이미지 변형 - 원근 #2

[반자동 문서 스캐너 프로젝트]
(2:32:50) 25.미니 프로젝트 1 - #1 마우스 이벤트 등록
(2:42:06) 26.미니 프로젝트 1 - #2 기본 코드 완성
(2:49:54) 27.미니 프로젝트 1 - #3 지점 선 긋기
(2:55:24) 28.미니 프로젝트 1 - #4 실시간 선 긋기

[OpenCV 후반전]
(3:01:52) 29.이미지 변형 - 이진화 #1 Trackbar
(3:14:37) 30.이미지 변형 - 이진화 #2 임계값
(3:20:26) 31.이미지 변형 - 이진화 #3 Adaptive Threshold
(3:28:34) 32.이미지 변형 - 이진화 #4 오츠 알고리즘
(3:32:22) 33.이미지 변환 - 팽창
(3:41:10) 34.이미지 변환 - 침식
(3:45:56) 35.이미지 변환 - 열림 & 닫힘
(3:54:10) 36.이미지 검출 - 경계선
(4:05:08) 37.이미지 검출 - 윤곽선 #1 기본
(4:15:26) 38.이미지 검출 - 윤곽선 #2 찾기 모드
(4:20:46) 39.이미지 검출 - 윤곽선 #3 면적

[카드 검출 & 분류기 프로젝트]
(4:27:42) 40.미니프로젝트 2

(4:31:57) 41.퀴즈

[얼굴인식 프로젝트]
(4:41:25) 42.환경설정 및 기본 코드 정리
(4:54:48) 43.눈과 코 인식하여 도형 그리기
(5:10:42) 44.그림판 이미지 씌우기
(5:20:52) 45.캐릭터 이미지 씌우기
(5:33:10) 46.보충설명
(5:40:53) 47.마치며 (학습 참고 자료)
(5:42:18) 48.Outro

수업에 필요한 이미지, 동영상 자료 링크입니다.

고양이 이미지 : 
크기 : 640 x 390  
파일명 : img.jpg

고양이 동영상 : 
크기 : SD (360 x 640)  
파일명 : video.mp4

신문 이미지 : 
크기 : 1280 x 853  
파일명 : newspaper.jpg

카드 이미지 1 : 
크기 : 1280 x 1019  
파일명 : poker.jpg

책 이미지 : 
크기 : Small (640 x 853)  
파일명 : book.jpg

눈사람 이미지 : 
크기 : 1280 x 904  
파일명 : snowman.png

카드 이미지 2 : 
크기 : 640 x 408  
파일명 : card.png

퀴즈용 동영상 : 
크기 : HD (1280 x 720)  
파일명 : city.mp4

프로젝트용 동영상 : 
크기 : Full HD (1920 x 1080)  
파일명 : face_video.mp4

프로젝트용 캐릭터 이미지 :  
파일명 : right_eye.png (100 x 100), left_eye.png (100 x 100), nose.png (300 x 100)

무료 이미지 편집 도구 :
(Pixlr E -Advanced Editor)

#python #opencv 

Terry  Tremblay

Terry Tremblay


What is Flutter and why you should learn it?

Flutter is an open-source UI toolkit for mobile developers, so they can use it to build native-looking** Android and iOS** applications from the same code base for both platforms. Flutter is also working to make Flutter apps for Web, PWA (progressive Web-App) and Desktop platform (Windows,macOS,Linux).


Flutter was officially released in December 2018. Since then, it has gone a much stronger flutter community.

There has been much increase in flutter developers, flutter packages, youtube tutorials, blogs, flutter examples apps, official and private events, and more. Flutter is now on top software repos based and trending on GitHub.

Flutter meaning?

What is Flutter? this question comes to many new developer’s mind.


Flutter means flying wings quickly, and lightly but obviously, this doesn’t apply in our SDK.

So Flutter was one of the companies that were acquired by **Google **for around $40 million. That company was based on providing gesture detection and recognition from a standard webcam. But later when the Flutter was going to release in alpha version for developer it’s name was Sky, but since Google already owned Flutter name, so they rename it to Flutter.

Where Flutter is used?

Flutter is used in many startup companies nowadays, and even some MNCs are also adopting Flutter as a mobile development framework. Many top famous companies are using their apps in Flutter. Some of them here are





Reflectly app

Reflectly app

Abbey Road Studios

Abbey Road Studios

and many more other apps. Mobile development companies also adopted Flutter as a service for their clients. Even I was one of them who developed flutter apps as a freelancer and later as an IT company for mobile apps.

Flutter as a service

#dart #flutter #uncategorized #flutter framework #flutter jobs #flutter language #flutter meaning #flutter meaning in hindi #google flutter #how does flutter work #what is flutter

Semantic Similarity Framework for Knowledge Graph


Sematch is an integrated framework for the development, evaluation, and application of semantic similarity for Knowledge Graphs (KGs). It is easy to use Sematch to compute semantic similarity scores of concepts, words and entities. Sematch focuses on specific knowledge-based semantic similarity metrics that rely on structural knowledge in taxonomy (e.g. depth, path length, least common subsumer), and statistical information contents (corpus-IC and graph-IC). Knowledge-based approaches differ from their counterpart corpus-based approaches relying on co-occurrence (e.g. Pointwise Mutual Information) or distributional similarity (Latent Semantic Analysis, Word2Vec, GLOVE and etc). Knowledge-based approaches are usually used for structural KGs, while corpus-based approaches are normally applied in textual corpora.

In text analysis applications, a common pipeline is adopted in using semantic similarity from concept level, to word and sentence level. For example, word similarity is first computed based on similarity scores of WordNet concepts, and sentence similarity is computed by composing word similarity scores. Finally, document similarity could be computed by identifying important sentences, e.g. TextRank.


KG based applications also meet similar pipeline in using semantic similarity, from concept similarity (e.g. to entity similarity (e.g. Furthermore, in computing document similarity, entities are extracted and document similarity is computed by composing entity similarity scores.


In KGs, concepts usually denote ontology classes while entities refer to ontology instances. Moreover, those concepts are usually constructed into hierarchical taxonomies, such as DBpedia ontology class, thus quantifying concept similarity in KG relies on similar semantic information (e.g. path length, depth, least common subsumer, information content) and semantic similarity metrics (e.g. Path, Wu & Palmer,Li, Resnik, Lin, Jiang & Conrad and WPath). In consequence, Sematch provides an integrated framework to develop and evaluate semantic similarity metrics for concepts, words, entities and their applications.

Getting started: 20 minutes to Sematch

Install Sematch

You need to install scientific computing libraries numpy and scipy first. An example of installing them with pip is shown below.

pip install numpy scipy

Depending on different OS, you can use different ways to install them. After sucessful installation of numpy and scipy, you can install sematch with following commands.

pip install sematch
python -m

Alternatively, you can use the development version to clone and install Sematch with setuptools. We recommend you to update your pip and setuptools.

git clone
cd sematch
python install

We also provide a Sematch-Demo Server. You can use it for experimenting with main functionalities or take it as an example for using Sematch to develop applications. Please check our Documentation for more details.

Computing Word Similarity

The core module of Sematch is measuring semantic similarity between concepts that are represented as concept taxonomies. Word similarity is computed based on the maximum semantic similarity of WordNet concepts. You can use Sematch to compute multi-lingual word similarity based on WordNet with various of semantic similarity metrics.

from sematch.semantic.similarity import WordNetSimilarity
wns = WordNetSimilarity()

# Computing English word similarity using Li method
wns.word_similarity('dog', 'cat', 'li') # 0.449327301063
# Computing Spanish word similarity using Lin method
wns.monol_word_similarity('perro', 'gato', 'spa', 'lin') #0.876800984373
# Computing Chinese word similarity using  Wu & Palmer method
wns.monol_word_similarity('狗', '猫', 'cmn', 'wup') # 0.857142857143
# Computing Spanish and English word similarity using Resnik method
wns.crossl_word_similarity('perro', 'cat', 'spa', 'eng', 'res') #7.91166650904
# Computing Spanish and Chinese word similarity using Jiang & Conrad method
wns.crossl_word_similarity('perro', '猫', 'spa', 'cmn', 'jcn') #0.31023804699
# Computing Chinese and English word similarity using WPath method
wns.crossl_word_similarity('狗', 'cat', 'cmn', 'eng', 'wpath')#0.593666388463

Computing semantic similarity of YAGO concepts.

from sematch.semantic.similarity import YagoTypeSimilarity
sim = YagoTypeSimilarity()

#Measuring YAGO concept similarity through WordNet taxonomy and corpus based information content
sim.yago_similarity('','', 'wpath') #0.642
sim.yago_similarity('','', 'wpath') #0.544
#Measuring YAGO concept similarity based on graph-based IC
sim.yago_similarity('','', 'wpath_graph') #0.423
sim.yago_similarity('','', 'wpath_graph') #0.328

Computing semantic similarity of DBpedia concepts.

from sematch.semantic.graph import DBpediaDataTransform, Taxonomy
from sematch.semantic.similarity import ConceptSimilarity
concept = ConceptSimilarity(Taxonomy(DBpediaDataTransform()),'models/dbpedia_type_ic.txt')
concept.similarity('','', 'path')
concept.similarity('','', 'wup')
concept.similarity('','', 'li')
concept.similarity('','', 'res')
concept.similarity('','', 'lin')
concept.similarity('','', 'jcn')
concept.similarity('','', 'wpath')

Computing semantic similarity of DBpedia entities.

from sematch.semantic.similarity import EntitySimilarity
sim = EntitySimilarity()
sim.similarity('','') #0.409923677282

Evaluate semantic similarity metrics with word similarity datasets

from sematch.evaluation import WordSimEvaluation
from sematch.semantic.similarity import WordNetSimilarity
evaluation = WordSimEvaluation()
wns = WordNetSimilarity()
# define similarity metrics
wpath = lambda x, y: wns.word_similarity_wpath(x, y, 0.8)
# evaluate similarity metrics with SimLex dataset
evaluation.evaluate_metric('wpath', wpath, 'noun_simlex')
# performa Steiger's Z significance Test
evaluation.statistical_test('wpath', 'path', 'noun_simlex')
# define similarity metrics for Spanish words
wpath_es = lambda x, y: wns.monol_word_similarity(x, y, 'spa', 'path')
# define cross-lingual similarity metrics for English-Spanish
wpath_en_es = lambda x, y: wns.crossl_word_similarity(x, y, 'eng', 'spa', 'wpath')
# evaluate metrics in multilingual word similarity datasets
evaluation.evaluate_metric('wpath_es', wpath_es, 'rg65_spanish')
evaluation.evaluate_metric('wpath_en_es', wpath_en_es, 'rg65_EN-ES')

Evaluate semantic similarity metrics with category classification

Although the word similarity correlation measure is the standard way to evaluate the semantic similarity metrics, it relies on human judgements over word pairs which may not have same performance in real applications. Therefore, apart from word similarity evaluation, the Sematch evaluation framework also includes a simple aspect category classification. The task classifies noun concepts such as pasta, noodle, steak, tea into their ontological parent concept FOOD, DRINKS.

from sematch.evaluation import AspectEvaluation
from sematch.application import SimClassifier, SimSVMClassifier
from sematch.semantic.similarity import WordNetSimilarity

# create aspect classification evaluation
evaluation = AspectEvaluation()
# load the dataset
X, y = evaluation.load_dataset()
# define word similarity function
wns = WordNetSimilarity()
word_sim = lambda x, y: wns.word_similarity(x, y)
# Train and evaluate metrics with unsupervised classification model
simclassifier = SimClassifier.train(zip(X,y), word_sim)
evaluation.evaluate(X,y, simclassifier)

macro averge:  (0.65319812882333839, 0.7101245049198579, 0.66317566364913016, None)
micro average:  (0.79210167952791644, 0.79210167952791644, 0.79210167952791644, None)
weighted average:  (0.80842645056024054, 0.79210167952791644, 0.79639496616636352, None)
accuracy:  0.792101679528
             precision    recall  f1-score   support

    SERVICE       0.50      0.43      0.46       519
 RESTAURANT       0.81      0.66      0.73       228
       FOOD       0.95      0.87      0.91      2256
   LOCATION       0.26      0.67      0.37        54
   AMBIENCE       0.60      0.70      0.65       597
     DRINKS       0.81      0.93      0.87       752

avg / total       0.81      0.79      0.80      4406

Matching Entities with type using SPARQL queries

You can use Sematch to download a list of entities having a specific type using different languages. Sematch will generate SPARQL queries and execute them in DBpedia Sparql Endpoint.

from sematch.application import Matcher
matcher = Matcher()
# matching scientist entities from DBpedia
matcher.match_type('científico', 'spa')
matcher.match_type('科学家', 'cmn')
matcher.match_entity_type('movies with Tom Cruise')

Example of automatically generated SPARQL query.

SELECT DISTINCT ?s, ?label, ?abstract WHERE {
    ?s <> <> . }
 UNION {  
    ?s <> <> . }
 UNION {  
    ?s <> <> . }
 UNION {  
    ?s <> <> . }
 UNION {  
    ?s <> <> . } 
    ?s <> <> . 
    ?s <> ?label . 
    FILTER( lang(?label) = "en") . 
    ?s <> ?abstract . 
    FILTER( lang(?abstract) = "en") .
} LIMIT 5000

Entity feature extraction with Similarity Graph

Apart from semantic matching of entities from DBpedia, you can also use Sematch to extract features of entities and apply semantic similarity analysis using graph-based ranking algorithms. Given a list of objects (concepts, words, entities), Sematch compute their pairwise semantic similarity and generate similarity graph where nodes denote objects and edges denote similarity scores. An example of using similarity graph for extracting important words from an entity description.

from sematch.semantic.graph import SimGraph
from sematch.semantic.similarity import WordNetSimilarity
from sematch.nlp import Extraction, word_process
from sematch.semantic.sparql import EntityFeatures
from collections import Counter
tom = EntityFeatures().features('')
words = Extraction().extract_nouns(tom['abstract'])
words = word_process(words)
wns = WordNetSimilarity()
word_graph = SimGraph(words, wns.word_similarity)
word_scores = word_graph.page_rank()
words, scores =zip(*Counter(word_scores).most_common(10))
print words
(u'picture', u'action', u'number', u'film', u'post', u'sport', 
u'program', u'men', u'performance', u'motion')


Ganggao Zhu, and Carlos A. Iglesias. "Computing Semantic Similarity of Concepts in Knowledge Graphs." IEEE Transactions on Knowledge and Data Engineering 29.1 (2017): 72-85.

Oscar Araque, Ganggao Zhu, Manuel Garcia-Amado and Carlos A. Iglesias Mining the Opinionated Web: Classification and Detection of Aspect Contexts for Aspect Based Sentiment Analysis, ICDM sentire, 2016.

Ganggao Zhu, and Carlos Angel Iglesias. "Sematch: Semantic Entity Search from Knowledge Graph." SumPre-HSWI@ ESWC. 2015.


You can post bug reports and feature requests in Github issues. Make sure to read our guidelines first. This project is still under active development approaching to its goals. The project is mainly maintained by Ganggao Zhu. You can contact him via gzhu [at]

Why this name, Sematch and Logo?

The name of Sematch is composed based on Spanish "se" and English "match". It is also the abbreviation of semantic matching because semantic similarity metrics helps to determine semantic distance of concepts, words, entities, instead of exact matching.

The logo of Sematch is based on Chinese Yin and Yang which is written in I Ching. Somehow, it correlates to 0 and 1 in computer science.

Author: Gsi-upm
Source Code: 
License: View license

#python #jupyternotebook #graph 

Armani  Conroy

Armani Conroy


Flutter's Complex Navigator 2.0 | Velocity X And V Router | In Hindi By Desi Programmer

In this video We are going to talk about Flutter’s nav 2.0 , routeInformationParser , router delegate and other packages that save us from Writting a lot of boiler plater code .

========== For this Video ==========

⏲ Time Stamps

  • 00:00 : Start
  • 00:20 : Differences
  • 01:26 : Bare Bone Code
  • 02:58: Awesome Packages
  • 04:38 : Remove #
  • 05:10 : Using Velocity_X
  • 19:45: V Router